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The AI Product Manager's Handbook

You're reading from  The AI Product Manager's Handbook

Product type Book
Published in Feb 2023
Publisher Packt
ISBN-13 9781804612934
Pages 250 pages
Edition 1st Edition
Languages
Author (1):
Irene Bratsis Irene Bratsis
Profile icon Irene Bratsis

Table of Contents (19) Chapters

Preface 1. Part 1 – Lay of the Land – Terms, Infrastructure, Types of AI, and Products Done Well
2. Chapter 1: Understanding the Infrastructure and Tools for Building AI Products 3. Chapter 2: Model Development and Maintenance for AI Products 4. Chapter 3: Machine Learning and Deep Learning Deep Dive 5. Chapter 4: Commercializing AI Products 6. Chapter 5: AI Transformation and Its Impact on Product Management 7. Part 2 – Building an AI-Native Product
8. Chapter 6: Understanding the AI-Native Product 9. Chapter 7: Productizing the ML Service 10. Chapter 8: Customization for Verticals, Customers, and Peer Groups 11. Chapter 9: Macro and Micro AI for Your Product 12. Chapter 10: Benchmarking Performance, Growth Hacking, and Cost 13. Part 3 – Integrating AI into Existing Non-AI Products
14. Chapter 11: The Rising Tide of AI 15. Chapter 12: Trends and Insights across Industry 16. Chapter 13: Evolving Products into AI Products 17. Index 18. Other Books You May Enjoy

Accuracy – optimizing for success

When it comes to DL, we can only truly grapple with its performance. Even from a performance perspective, a lot of DL projects fail to give the results their own engineers are hoping for, so it’s important to manage expectations. This is doubly true if you’re managing the expectations of your leadership team as well. If you’re a product manager or entrepreneur and you’re thinking of incorporating DL, do so in the spirit of science and curiosity. Remain open about your expectations.

But make sure you’re setting your team up for success. A big part of your ANN’s performance also lies in the data preparation you take before you start training your models. Passing your data through an ANN is the last step in your pipeline. If you don’t have good validation or if the quality of your data is poor, you’re not going to see positive results. Then, once you feel confident that you have enough...

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